A tablet PC, or pen computer, is a notebook or slate-shaped mobile computer, equipped with a touch screen or graphics tablet/screen hybrid technology that allows the user to operate the computer with a stylus, digital pen, or fingertip instead of a keyboard or mouse. Tablet PCs offer a more natural form of input, as sketching and handwriting are a much more familiar form of input than a keyboard and mouse, especially for people who are new to computers. Tablet PCs can also be more accessible because those who are physically unable to type can utilize the additional features of a tablet PC to be able to interact with the electronic world.
Natural input applications are available that store user handwriting on a tablet PC. Handwriting recognition is the process of receiving and interpreting natural handwritten input, then converting it to input suitable for computer processing. For example, handwriting recognition software may convert handwritten script into characters of the ASCII or Unicode character set. Recognition alternatives are the result of the recognition process. For every ink input, the handwriting recognition software can produce an arbitrary number of recognition alternatives (recognition results).
One challenge in recognizing handwriting is determining where a character ends and a new character begins. Cursive handwriting includes continuous writing of whole words. One common way to recognize individual characters in a word is to segment ink into atomic elements that can be single letters or their parts. A particular letter may be made up of multiple identified segments. A beginning segment is the first ink segment that belongs to a character. Very short characters like the dot have only the beginning segment. A continuation segment includes any ink segment after the first one that forms part of a character. Word breaking is a similar process that distinguishes individual words in a piece of ink. Word breaking is often easier than segmentation because words are generally divided by whitespace.
Due to the nature of handwriting and the variety of handwriting styles, even the best handwriting recognizers cannot accomplish reasonable accuracy without applying some kind of a language model. A language model is a component of handwriting recognition software that scores recognition alternatives based on a set of rules associated with a target language. The purpose of the language model is to increase recognition accuracy by applying language-dependent constraints to the recognition results. Constraints of a language model are designed to suppress recognition alternatives that are impossible or rare in the target language. For example, an English handwriting recognizer may tend to prefer recognizing a two-letter word as “is” rather than “ic” where both are possible recognition results, because “is” is a word in the English language, and “ic” is not.
Some handwriting recognition software uses language models based on dictionaries and sets of rules for combining words from the dictionaries. Although this kind of software does a good job for a set of supported words, it often fails to recognize words that are not in the dictionary. Human handwriting often contains words that are not part of the language model. For example, handwriting may include foreign names, names of companies, foreign cities, and other words that are not in the language model.
Time Delayed Neural Network (TDNN) is classifier that can be used to perform shape classification of the ink segments. A TDNN is a group of neural networks with a special topology that are designed to independently recognize feature units within a larger pattern. Except for the standard set of connections for a current feature unit, TDNNs have connections to input and hidden layers of neighbor feature units.